极端气候影响下黄河中下游地区宜居水平时空变化

doi:10.12118/j.issn.1000-6060.2025.403

干旱区地理 ›› 2026, Vol. 49 ›› Issue (5): 975-986.doi: 10.12118/j.issn.1000-6060.2025.403 cstr: 32274.14.ALG2025403

极端气候影响下黄河中下游地区宜居水平时空变化

宁晓菊¹(), 张立², 杨璐瑶³, 毛齐正¹(), 刘晓卓¹

¹ 河南财经政法大学城乡规划学院，河南郑州 450046
² 中国地震局地震研究所，湖北武汉 430071
³ 中国科学院大学资源与环境学院，北京 100049

收稿日期:2025-07-14 修回日期:2025-08-18 出版日期:2026-05-25 发布日期:2026-05-25
通讯作者: 毛齐正（1984-），博士，副教授，主要从事城市生态系统服务健康福祉研究. E-mail: maoqizhenger@126.com
作者简介:宁晓菊（1987-），博士，讲师，主要从事气候变化与韧性研究. E-mail: nxj0655@163.com
基金资助:
国家自然科学基金面上项目(32471662);国家自然科学基金面上项目(42576275);河南省科技攻关(252102110152);河南省科技攻关(232102110039);河南省自然科学基金青年项目(252300423230)

Spatiotemporal variations of livability level in the middle and lower reaches of the Yellow River under the impact of extreme climate

NING Xiaoju¹(), ZHANG Li², YANG Luyao³, MAO Qizheng¹(), LIU Xiaozhuo¹

¹ School of Urban and Rural Planning, Henan University of Economics and Law, Zhengzhou 450046, Henan, China
² Institute of Seismology, China Earthquake Administration, Wuhan 430071, Hubei, China
³ College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2025-07-14 Revised:2025-08-18 Published:2026-05-25 Online:2026-05-25

摘要/Abstract

摘要：

评估气候宜居水平、分析极端气候对宜居性的影响，是改善城乡人居环境的关键。以黄河中下游地区为例，根据1990—2022年493个气象站逐日观测数据，通过气候适宜性因子揭示气候宜居水平的分布，利用最大熵（MaxEnt）模型模拟极端气候对宜居性的影响。结果表明：（1）黄河中下游地区气候宜居水平多年平均值呈南高北低、东高西低分布。33 a来，气候宜居水平的变化趋势在中游北部以不显著上升为主，在中游南部及下游地区以微显著下降为主。（2）中游北部夏季减湿、1月升温及年适宜温度天数增加，中游南部和下游地区夏季增温和冬季日照适宜性下降导致热不舒适上升，分别是二者气候宜居水平变化的主因。（3）极端高温、极端低温和极端降水对黄河中下游地区气候宜居性的影响均以中和高强度为主，在空间上呈南高北低、东高西低分布。但极端降水影响强度的年际变幅最大，极端低温次之，极端高温最小。（4）相对于中游北部，中游南部和下游地区热浪天数、极端降水量和极端降水天数的发生频率较高，霜冻天数和寒冷天数差值大是该区气候宜居性受极端气候影响高的主因。

关键词: 适宜性因子, 气候宜居水平, 极端气候, 影响强度, 时空变化, 黄河中下游

Abstract:

Assessing climate livability and analyzing the impacts of extreme climates on livability are critical for improving the human settlements in both urban and rural areas. Using the middle and lower reaches of the Yellow River in China as the study area, this study maps the spatial distribution of climate livability based on climatic suitability factors and then simulates the impacts of extreme climates on livability using the MaxEnt model. The analysis utilizes daily observational data from 493 meteorological stations from 1990 to 2022. The main findings are as follows: (1) The multi-year average climate livability in the Yellow River was higher in the south and east and lower in the north and west. Over 33 years, livability trends were non-significantly upward in the northern middle reaches, but slightly significantly downward in the southern middle reaches and lower reaches. (2) Variations in climate livability were driven by decreased summer humidity, high January temperatures, and increased suitable-temperature days in the northern middle reaches, as opposed to intensified summer warming and reduced sunshine suitability in winter in the southern middle reaches and lower reaches. (3) Extreme high and low temperatures and extreme precipitation generally had moderate-to-high impacts on livability, with strong effects in the south and east and weaker effects in the north and west. Interannual variability was highest for extreme precipitation, followed by extreme low temperatures, and lowest for extreme high temperatures. (4) Compared to the northern middle reaches, the southern middle reaches and lower reaches were more affected by extreme climate due to the higher frequency of heatwave days, extreme precipitation amounts, and extreme precipitation days. The large discrepancies between frost days and cold days is the main reason why the climate livability of this area is highly affected by extreme climate.

Key words: suitability factors, climate livability level, extreme climate, impact intensity, spatiotemporal variations, middle and lower reaches of the Yellow River

宁晓菊, 张立, 杨璐瑶, 毛齐正, 刘晓卓. 极端气候影响下黄河中下游地区宜居水平时空变化[J]. 干旱区地理, 2026, 49(5): 975-986.

NING Xiaoju, ZHANG Li, YANG Luyao, MAO Qizheng, LIU Xiaozhuo. Spatiotemporal variations of livability level in the middle and lower reaches of the Yellow River under the impact of extreme climate[J]. Arid Land Geography, 2026, 49(5): 975-986.

图/表 9

图1

表1

图2

表2

表3

图3

图4

图5

表4

参考文献 31

[1]	Wang Y J, Chen Y, Hewitt C, et al. Climate services for addressing climate change: Indication of a climate livable city in China[J]. Advances in Climate Change Research, 2021, 12(5): 744-751. doi: 10.1016/j.accre.2021.07.006
[2]	Vanos J, Guzman-Echavarria G, Baldwin J W, et al. A physiological approach for assessing human survivability and livability to heat in a changing climate[J]. Nature Communications, 2023, 14: 7653, doi: 10.1038/s41467-023-43121-5.
[3]	Chen W Z, Yi L, Wang J Y, et al. Evaluation of the livability of arid urban environments under global warming: A multi-parameter approach[J]. Sustainable Cities and Society, 2023, 99: 104931, doi: 10.1016/j.scs.2023.104931.
[4]	刘圆, 王业成, 袁绮菲, 等. 南京江北核心区气候环境宜居水平评价[J]. 环境工程, 2017, 35(5): 145-148.
	[Liu Yuan, Wang Yecheng, Yuan Qifei, et al. Assessment of climatic and environmental livability in core areas of Nanjing Jiangbei New District[J]. Environmental Engineering, 2017, 35(5): 145-148.]
[5]	Zhang Y C, Xiao F, Mei H, et al. Comprehensive analysis of climate-related comfort in southern China: Climatology, trend, and interannual variations[J]. Urban Climate, 2022, 46: 101349, doi: 10.1016/j.uclim.2022.101349.
[6]	贾妮娅·叶力肯, 侯建楠, 刘思博. 近30 a新疆地州市旅游气候舒适度时空特征分析[J]. 干旱区地理, 2025, 48(2): 212-222. doi: 10.12118/j.issn.1000-6060.2024.086
	[Yerken Jianiya, Hou Jiannan, Liu Sibo. Spatio-temporal characterization of tourism climate comfort in Xinjiang prefectures and cities in the last 30 years[J]. Arid Land Geography, 2025, 48(2): 212-222.] doi: 10.12118/j.issn.1000-6060.2024.086
[7]	Singh A, Chandra T, Mathur S, et al. Determination of outdoor thermal comfort thresholds for hot and semi-arid climates: A field study of residential neighborhoods in Jaipur City[J]. Sustainable Cities and Society, 2024, 115: 105817, doi: 10.1016/j.scs.2024.105817.
[8]	Elsawy A A, Ayad H M, Saadallah D. Assessing livability of residential streets-case study: El-Attarin, Alexandria, Egypt[J]. Alexandria Engineering Journal, 2019, 58(2): 745-755. doi: 10.1016/j.aej.2019.06.005
[9]	赵晓龙, 卞晴, 侯韫婧, 等. 寒地城市公园春季休闲体力活动水平与微气候热舒适关联研究[J]. 中国园林, 2019, 35(4): 80-85.
	[Zhao Xiaolong, Bian Qing, Hou Yunjing, et al. A research on the correlation between physical activity performance and thermal comfortable of urban park in cold region[J]. Chinese Landscape Architecture, 2019, 35(4): 80-85.]
[10]	Mostafa S, Kashi H, Farrokhzadeh S, et al. Effects of extreme weather events and climate change on cities’ livability[J]. Cities, 2024, 151: 105114, doi: 10.1016/j.cities.2024.105114.
[11]	Filho W L, Tuladhar L, Li C L, et al. Climate change and extremes: Implications on city livability and associated health risks across the globe[J]. International Journal of Climate Change Strategies and Management, 2022, 15(1): 1-19. doi: 10.1108/IJCCSM-07-2021-0078
[12]	Alijani S, Pourahmad A, Nejad H H, et al. A new approach of urban livability in Tehran: Thermal comfort as a primitive indicator. Case study, district 22[J]. Urban Climate, 2020, 33: 100656, doi: 10.1016/j.uclim.2020.100656.
[13]	Shi C C, Guo N L, Zeng L L, et al. How climate change is going to affect urban livability in China[J]. Climate Services, 2022, 26: 100284, doi: 10.1016/j.cliser.2022.100284.
[14]	王旭, 付学成, 徐文甜, 等. 2000—2020年中国城乡热舒适梯度特征及其驱动因素[J]. 地理学报, 2024, 79(5): 1318-1336. doi: 10.11821/dlxb202405014
	[Wang Xu, Fu Xuecheng, Xu Wentian, et al. The signatures and drivers of thermal comfort acrossthe urban-rural gradient in Chinese citiesfrom 2000 to 2020[J]. Acat Geographica Scinica, 2024, 79(5): 1318-1336.]
[15]	贺山峰, 陈超冰, 李铮, 等. 黄河中上游极端降水特征及其对区域气候变化的敏感性[J]. 资源科学, 2024, 46(3): 524-537. doi: 10.18402/resci.2024.03.07
	[He Shanfeng, Chen Chaobing, Li Zheng, et al. Characteristics of extreme precipitation and its sensitivity to regional climate change in the upper and middle reaches of the Yellow River Basin[J]. Resources Science, 2024, 46(3): 524-537.] doi: 10.18402/resci.2024.03.07
[16]	刘盼, 赵西宁, 高晓东, 等. 黄土高原极端气温变化特征及其与评价气温的相关性[J]. 应用生态学报, 2022, 33(7): 1975-1982. doi: 10.13287/j.1001-9332.202207.024
	[Liu Pan, Zhao Xining, Gao Xiaodong, et al. Characteristics of extreme temperature variation in the Loess Plateau and its correlation with average temperature[J]. Chinese Journal of Applied Ecology, 2022, 33(7): 1975-1982.] doi: 10.13287/j.1001-9332.202207.024
[17]	Ghasemi K, Hamzenejad M, Meshkini A. The spatial analysis of the livability of 22 districts of Tehran metropolis using multi-criteria decision making approaches[J]. Sustainable Cities and Society, 2018, 38: 382-404. doi: 10.1016/j.scs.2018.01.018
[18]	Savari M, Moradi M. The effectiveness of drought adaptation strategies in explaining the livability of Iranian rural households[J]. Habitat International, 2022, 124: 102560, doi: 10.1016/j.habitatint.2022.102560.
[19]	Xiao C, Wu P L, Zhang L X, et al. Robust increase in extreme summer rainfall intensity during the past four decades observed in China[J]. Scientific Reports, 2016, 6: 38506, doi: 10.1038/srep38506. pmid: 27917927
[20]	于群, 孙越, 李建平, 等. 秋季黄河中下游降水主模态及2021年极端降水的气候背景[J]. 气象学报, 2023, 81(4): 547-558.
	[Yu Qun, Sun Yue, Li Jianping, et al. The leading mode of autumn rainfall over the midlower reaches of the Yellow River and the climate background of extreme autumn rainfall in 2021[J]. Acta Meteorologica Sinica, 2023, 81(4): 547-558.]
[21]	黄晓军, 王博, 刘萌萌, 等. 中国城市高温特征及社会脆弱性评价[J]. 地理研究, 2020, 39(7): 1534-1547. doi: 10.11821/dlyj020190608
	[Huang Xiaojun, Wang Bo, Liu Mengmeng, et al. Characteristics of urban extreme heat and assessment of social vulnerability in China[J]. Geographical Research, 2020, 39(7): 1534-1547.]
[22]	Ma F, Yuan X, Li H. Characteristics and circulation patterns for wet and dry compound day-night heat waves in mid-eastern China[J]. Global and Planetary Change, 2022, 213: 103839, doi: 10.1016/j.gloplacha.2022.103839.
[23]	王淼淼, 丁明虎, 吕俊梅, 等. 近40年中国冬季寒潮的气候特征及大气环流异常[J]. 应用气象学报, 2024, 35(3): 298-310.
	[Wang Miaomiao, Ding Minghu, Lü Junmei, et al. Climatology of winter cold waves and associated atmospheric circulation anomalies in China during the last 40 years[J]. Journal of Applied Meteorological Science, 2024, 35(3): 298-310.]
[24]	Lu Y F, Li J, Song Z Q, et al. Evaluating sustainable development in the middle and lower reaches of the Yellow River Basin using multiple data sources[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 1-18.
[25]	GB/T 42072-2022. 中华人民共和国国家标准: 气候宜居指数[S]. 北京: 中国标准出版社, 2022.
	[GB/T 42072-2022. National standard of the People’s Republic of China: Climate livability index[S]. Beijing: Standards Press of China, 2022.]
[26]	GB/T 27963-2011. 中华人民共和国国家标准: 人居环境舒适度评价[S]. 北京: 中国标准出版社, 2021.
	[GB/T 27963-2011. National standard of the People’s Republic of China: Climatic suitability evaluating on human settlement[S]. Beijing: Standards Press of China, 2021.]
[27]	Phillips S J, Anderson R P, Schapire R E. Maximum entropy modeling of species geographic distributions[J]. Ecological Modelling, 2006, 190(3-4): 231-259. doi: 10.1016/j.ecolmodel.2005.03.026
[28]	IPCC Working Group II to the Fifth Assessment Report of the Intergouvermental Panel on Climate Change. Climate change 2014: Impacts, adaptation, and vulnerability[M]. Cambridge: Cambridge University Press, 2014: 1-696.
[29]	安彬, 肖薇薇, 刘宇峰, 等. 1955—2021年黄土高原地区相对湿度时空演变规律[J]. 干旱区地理, 2023, 46(12): 1939-1950. doi: 10.12118/j.issn.1000-6060.2023.105
	[An Bin, Xiao Weiwei, Liu Yufeng, et al. Temporal and spatial evolution of relative humidity in the Loess Plateau during 1955—2021[J]. Arid Land Geography, 2023, 46(12): 1939-1950.] doi: 10.12118/j.issn.1000-6060.2023.105
[30]	谭凯炎, 房世波, 俄有浩. 中国主要城市气候舒适度时空特征[J]. 气象与环境科学, 2024, 47(3): 1-7.
	[Tan Kaiyan, Fang Shibo, E Youhao. Spatio-temporal features of climate comfortability in major cities of China[J]. Meteorological and Environmental Sciences, 2024, 47(3): 1-7.]
[31]	薛东前, 王莎, 王佳宁, 等. 黄土高原乡村“人水土”系统协同与机制[J]. 资源科学, 2022, 44(9): 1809-1823.
	[Xue Dongqian, Wang Sha, Wang Jianing, et al. Coordination of human-water-land system and mechanism in rural areas of the Loess Plateau[J]. Resources Science, 2022, 44(9): 1809-1823.] doi: 10.18402/resci.2022.09.06

类别	类型	指标因子	含义	特征
气候宜居水平	年际水热适宜性	年平均气温/℃	一年中日平均气温的平均值	区间
		年适宜温度天数/d	一年中日平均气温在15~25 ℃的天数	正向
		年适宜降水天数/d	一年中日降水量在0.1~10 mm的天数	正向
		年适宜湿度天数/d	一年中日相对湿度在50%~80%的天数	正向
		降水季节均匀度	冬季降水量与夏季降水量的比值	正向
	夏季湿热适宜性	夏季平均气温日较差/℃	夏季日最高气温与日最低气温差值的平均值	正向
		夏季平均相对湿度/%	夏季空气相对湿度的平均值	反向
		7月平均最低气温/℃	7月日最低气温的平均值	区间
	冬季光热适宜性	冬季日照总时数/h	11、12月和次年1月日照时数的总和	正向
	冬季光热适宜性	1月平均最高气温/℃	1月日最高气温的平均值	正向
极端气候影响	极端高温	年高温天数/d	一年中日最高气温≥35 ℃的天数	负向
		年热浪天数/d	一年中热浪事件的总天数	负向
		年热浪频次/次	一年中热浪事件发生的总次数	负向
	极端低温	年霜冻天数/d	一年中日最低气温≤0 ℃的天数	负向
	极端低温	年寒冷天数/d	一年中风效指数<-400的天数	负向
	极端降水	年极端降水量/mm	一年中日降水量≥50 mm的降水量的和	负向
		年极端降水天数/d	一年中日降水量≥50 mm的天数	负向
		最大连续干日/d	一年中最长一次持续无降水日数	负向

变化趋势	显著性	年平均气温		年适宜温度天数		年适宜降水天数		年适宜湿度天数		降水季节均匀度
变化趋势	显著性	站点数量	比例/%	站点数量	比例/%	站点数量	比例/%	站点数量	比例/%	站点数量	比例/%
上升	显著	371	75.25	301	61.06	219	44.42	107	21.70	204	41.38
上升	不显著	69	14.00	139	28.19	160	32.45	79	16.03	133	26.98
下降	显著	18	3.65	14	2.84	34	6.90	218	44.22	54	10.95
下降	不显著	35	7.10	39	7.91	80	16.23	89	18.05	102	20.69

变化趋势	显著性	夏季平均相对湿度		夏季平均气温日较差		7月平均最低气温		冬季日照总时数		1月平均最高气温
变化趋势	显著性	站点数量	比例/%	站点数量	比例/%	站点数量	比例/%	站点数量	比例/%	站点数量	比例/%
上升	显著	48	9.74	102	20.69	324	65.72	34	6.90	135	27.38
上升	不显著	47	9.53	90	18.25	90	18.26	46	9.33	222	45.03
下降	显著	309	62.68	200	40.57	31	6.28	342	69.37	16	3.25
下降	不显著	89	18.05	101	20.49	48	9.74	71	14.40	120	24.34

高影响类型	1991—1995年	1996—2000年	2001—2005年	2006—2010年	2011—2015年	2016—2020年
极端降水、高温和低温高影响	20.30	18.60	18.73	20.79	20.31	18.40
极端高温和低温高影响	9.32	3.65	4.12	5.71	10.16	12.00
极端降水和高温高影响	0.61	2.39	3.77	1.41	2.75	0.00
极端降水和低温高影响	5.15	9.19	8.93	11.19	8.80	0.00
极端高温高影响	3.78	3.95	4.53	3.20	3.36	0.00
极端低温高影响	7.22	7.89	5.93	5.31	7.37	0.00
极端降水高影响	2.22	4.91	5.23	13.70	6.28	13.61
非高影响	51.40	49.42	48.76	38.69	40.97	55.99

极端气候影响下黄河中下游地区宜居水平时空变化

Spatiotemporal variations of livability level in the middle and lower reaches of the Yellow River under the impact of extreme climate

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 31

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	次旺, 杜军, 扎西顿珠, 陈鲜艳, 肖卓靖, 平措桑旦, 刘赛. 西藏气候基准值更新的气候响应与业务影响[J]. 干旱区地理, 2026, 49(5): 907-916.
[2]	李若楠, 李均力, 刘帅琪, 都伟冰. 1960—2023年木孜塔格峰冰川变化及其地形因素分析[J]. 干旱区地理, 2026, 49(1): 23-34.
[3]	黄静, 李婷, 李朋飞, Altansukh OCHIR, 杨梅焕, 王涛, 李莎. 2000—2020年蒙古国植被净初级生产力时空演变特征及其影响因素[J]. 干旱区地理, 2025, 48(9): 1541-1554.
[4]	刘京会, 袁旭山, 李艳敏, 李鑫旭. 基于CMIP6的伊犁河流域极端降水时空特征分析研究[J]. 干旱区地理, 2025, 48(8): 1329-1341.
[5]	张毅明, 汤宇磊, 冯俊波. 基于随机森林模型的青藏高原冰川预测及分析[J]. 干旱区地理, 2025, 48(8): 1342-1352.
[6]	赵建文, 李金麟, 王圣杰. 祁连山土壤水分时空变化特征及主要驱动因素分析[J]. 干旱区地理, 2025, 48(8): 1480-1491.
[7]	张昕晗, 赵文婷, 焦菊英, 马晓武, 杨波, 凌麒. 1960—2023年黄土高原极端降水事件时空演变特征[J]. 干旱区地理, 2025, 48(7): 1153-1166.
[8]	魏建飞, 袁悠燃, 李强, 董佩佩, 刘玖榕. 黄河流域国土空间效率时空变化及障碍因子分析[J]. 干旱区地理, 2025, 48(6): 1055-1066.
[9]	卢晗, 曾永年, 王盼成. 青藏高原东北部实际蒸散发时空变化特征及影响因素[J]. 干旱区地理, 2025, 48(5): 753-764.
[10]	李梦冉, 徐小任, 王梁, 段健, 史舒琪, 任丹丹. 黄河流域农业碳排放时空变化特征及影响因素分析[J]. 干旱区地理, 2025, 48(5): 854-865.
[11]	侯迎, 刘雯惠, 褚阳, 马小娟, 姚诗雨, 倪同欣. 贺兰山东麓绿洲多层次土壤水分亏缺及其影响因素的时空分析[J]. 干旱区地理, 2025, 48(4): 649-660.
[12]	闫劲烨, 马正权, 孙萱萱, 阿力木·阿巴斯, 帕丽达·牙合甫. 2015—2023年“乌-昌-石”城市群PM_2.5与PM₁₀时空变化及潜在源分析[J]. 干旱区地理, 2025, 48(3): 405-420.
[13]	马启民, 杜函芮, 王峥铭, 龙银平. 鄂尔多斯高原十大孔兑不同地貌地形的植被覆盖特征分析[J]. 干旱区地理, 2025, 48(3): 434-443.
[14]	李康宁, 林伊琳, 赵俊三, 王健, 葛峰. 三江源植被覆盖变化驱动机制及生态脆弱性分析[J]. 干旱区地理, 2025, 48(2): 283-295.
[15]	张海东, 李崇博, 孟李奇, 阿地来·赛提尼亚孜, 巨喜锋. 喀什三角洲NDVI演变特征及其对气候的响应[J]. 干旱区地理, 2025, 48(2): 296-307.